Mobile devices with various methods of connectivity are now for many people becoming the primary gateway to the internet and also a major storage point for personal information. This is in addition to the normal range of personal computers and furthermore sensor devices plus internet based providers. Combining these devices together and lately the applications and the information stored by those applications is a major challenge of interoperability. This can be achieved through numerous, individual and personal information spaces in which persons, groups of persons, etc. can place, share, interact and manipulate (or program devices to automatically perform the planning, interaction and manipulation of) webs of information with their own locally agreed semantics without necessarily conforming to an unobtainable, global whole.
Furthermore, in addition to information, the information spaces may be combined with webs of shared and interactive computations or computation spaces so that the devices having connectivity to the computation spaces can have the information in the information space manipulated within the computation space environment and the results delivered to the device, rather than the whole process being performed locally in the device. It is noted that such computation spaces may consist of connectivity between devices, from devices to network infrastructure, to distributed information spaces so that computations can be executed where enough computational elements are available. These combined information spaces and computation spaces often referred to as computation clouds, are extensions of the ‘Giant Global Graph’ in which one can apply semantics and reasoning at a local level.
In one example, clouds are working spaces respectively embedded with distributed information and computation infrastructures spanned around computers, information appliances, processing devices and sensors that allow people to work efficiently through access to information and computations from computers or other devices. An information space or a computation space can be rendered by the computation devices physically presented as heterogeneous networks (wired and wireless). However, despite the fact that information and computation presented by the respective spaces can be distributed with different granularity, still there are challenges in certain example implementations to achieve scalable high context information processing within such heterogeneous environments. For example, in various implementations, due to distributed nature of the cloud, exchange of data, information, and computation elements (e.g., computation closures) among distributed devices involved in a cloud infrastructure may require excessive amount of resources (e.g. process time, process power, storage space, etc.). In various example circumstances, to enhance the information processing power of a device and reduce the processing cost, one might consider minimizing or at least significantly improving exchange of data, information and computations among the distributed devices. In various example embodiments we can minimize or improve or significantly improve data migration within a computational architecture by providing multi-level distributed computations, such that the data can be migrated to the closest possible computation level with minimized or improved cost. However, unbalanced distribution of computations within a computation architecture may cause congestions in parts of the architecture while there may be unused available resources in other parts of the architecture. Furthermore, under certain conditions some of the resources within the architecture may become unavailable, for example due to faults, failures, heavy transaction load, etc.